Predictions of future value are critical in industries ranging from business planning to stock market prediction to health outcomes to military outcomes to political outcomes to horse racing.
For instance, Monte Carlo methods are used in insurance, investment, and other industries to predict likely outcome or future value. Monte Carlo methods (or Monte Carlo experiments) are a class of computational algorithms that rely on repeated random sampling to estimate likely outcome. Monte Carlo methods are especially useful for simulating complex non-linear systems with coupled interactions. These methods have been used to model phenomena with significant uncertainty in inputs, such as the calculation of risk in business. Related patents in this area include U.S. Pat. Nos. 8,095,392, 8,036,975, and their precedents.
In theory, Monte Carlo systems can handle arbitrarily large and complex systems provided sufficient rules and interactions are defined. In practice, often hundreds of thousands of trials need to be run to get rough approximations of likely outcomes. For example, 10 independent input variables trialed 10 times each yields 10 million system trial outputs (10{circumflex over ( )}10), yet if effects related to a single input are significantly non-linear, it is possible that maxima and minima will be missed by this rough sampling. Therefore, Monte Carlo-based predictions are costly in terms of the time and resources needed to perform the calculations. Further, many computer-based Monte Carlo methods require explicit statements (computer programmed rules) defining the complex relationships between inputs and outputs.
Neural Nets have been used to replace programmed rules by using learning sets to train the nets. In this approach, data replaces knowledge and understanding of interactions—the programmed rules. But, with sufficient and good training data, Neural Nets have been shown to predict not just optima, but also likeliness of outcome. Again, typically histogram approximations to probability distribution curves, surfaces, etc, are produced.
Genetic Algorithms, Particle Swarm methods, and other optimization techniques have been used to reduce the number of trials needed to find local and sometimes global optima, but typically at the expense of understanding interactions. Optimization methods work well when many inputs are involved to find the “best” but typically, these methods will not expose interactions or the solution space: A small cloud of points around “best outcome” is typically produced, along with tracks to that point, but a distribution describing the probability of the outcome or likelihood of second best—is not produced.
One major fault with most numerical methods—no matter how complex, is that they rely on a good understanding of the problem (the rules), or sufficient data to describe the solution space. Most numerical methods rely on explicit knowledge: who, what, where, when, how much, quantified statements of likelihood, facts. Yet most business decisions, and most other decisions people make, that is to say, attempts to predict future value or likelihood of outcomes (the subject of this patent), are made using both explicit knowledge and tacit knowledge. Explicit knowledge: who, what, where, when, how much, quantified statements of likelihood, facts. Tacit knowledge: why, beliefs, opinions, feelings, hunches, and other non-quantifiable statements of likelihood.
Methods have been developed that work to incorporate tacit domain expert knowledge, along with other processing tools like decision fault trees, decision maps, models, and other visualization tools like the process of U.S. Pat. No. 8,103,601. Fundamentally, these methods are trying to get at the fact that much of the knowledge used by business executives, and everyone else, to make decisions is tacit rather than explicit, and are incorporating expensive processes to turn that tacit knowledge into rules amenable to processing by logic trees and other numerical methods.
Business planners use methods such as Gantt charts, the Critical Path Method (CPM) and Program Evaluation Review Technique (PERT) to attempt to understand critical paths and time to completion. In one approach, the project planner assigns a minimum, maximum and expected duration to each task, earliest start and finish times and last start and finish times. A simulator calculates an ending date as a function of the minimum, maximum and expected durations, the earliest start and finish times and the last start and finish times. In one such approach, the minimum, maximum and expected duration assumptions for each task are modeled in the simulator using a triangular, beta or gamma distribution of possible duration values. Such an approach is described by Johnathan Mun in Advanced Analytical Models, John Wiley & Sons, Jun. 2, 2008. Such approaches are useful for scheduling but are less useful for predicting changes in value over time.